A Powreful Finite Mixture Model Based on the Generalized Dirichlet Distribution: Unsupervised Learning and Applications

Authors:
Nizar Bouguila;Djemel Ziou
Affiliations:
Université de Sherbrooke, Canada;Université de Sherbrooke, Canada
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

Citing 0
Cited 3

High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete data clustering using finite mixture models

Pattern Recognition
Online variational learning of generalized Dirichlet mixture models with feature selection

Neurocomputing

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Abstract

This paper presents a new finite mixture model based on a generalization of the Dirichlet distribution. For the estimation of the parameters of this mixture we use a GEM (Generalized Expectation Maximization) algorithm Based on a Newton-Raphson step. The experimental results involve the comparison of the performance of Gaussian and generalized Dirichlet mixtures in the classification of several pattern-recognition data sets.